ion of future large-sample studies examining CMI and the effectiveness of DTT interventions with CMI-APP in people with MS. ©Andrea Tacchino, Renee Veldkamp, Karin Coninx, Jens Brulmans, Steven Palmaers, Päivi Hämäläinen, Mieke D'hooge, Ellen Vanzeir, Alon Kalron, Giampaolo Brichetto, Peter Feys, Ilse Baert. Originally published in JMIR mHealth and uHealth (http//mhealth.jmir.org), 19.04.2020.BACKGROUND As the management of type 2 diabetes remains suboptimal in primary care, the Road to Hierarchical Diabetes Management at Primary Care (ROADMAP) study was designed and conducted in diverse primary care settings to test the effectiveness of a three-tiered diabetes management model of care in China. OBJECTIVE This paper aims to predetermine the detailed analytical methods for the ROADMAP study before the database lock to reduce potential bias and facilitate transparent analyses. METHODS The ROADMAP study adopts a community-based, cluster randomized controlled trial design that compares the effectiveness of a tiered diabetes management model on diabetes control with usual care among patients with diabetes over a 1-year study period. The primary outcome is the control rate of glycated hemoglobin (HbA1c) less then 7% at 1 year. Secondary outcomes include the control rates of ABC (HbA1c, blood pressure, and low-density lipoprotein cholesterol [LDL-C], individual and combined) and fasting blood glucose, alates for the main figure and tables are presented. CONCLUSIONS This statistical analysis protocol was developed for the main results of the ROADMAP study by authors blinded to group allocation and with no access to study data, which will guarantee the transparency and reduce potential bias during statistical analysis. TRIAL REGISTRATION Chinese Clinical Trial Registry ChiCTR-IOC-17011325; https//tinyurl.com/ybpr9xrq. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/18333. ©Xian Li, Nadila Duolikun, Fengzhuo Cheng, Laurent Billot, Weiping Jia, Puhong Zhang. https://www.selleckchem.com/products/gsk2795039.html Originally published in JMIR Research Protocols (http//www.researchprotocols.org), 28.04.2020.BACKGROUND Machine-learning or deep-learning algorithms for clinical diagnosis are inherently dependent on the availability of large-scale clinical datasets. Lack of such datasets and inherent problems such as overfitting often necessitate the development of innovative solutions. Probabilistic modeling closely mimics the rationale behind clinical diagnosis and represents a unique solution. OBJECTIVE The aim of this study was to develop and validate a probabilistic model for differential diagnosis in different medical domains. METHODS Numerical values of symptom-disease associations were utilized to mathematically represent medical domain knowledge. These values served as the core engine for the probabilistic model. For the given set of symptoms, the model was utilized to produce a ranked list of differential diagnoses, which was compared to the differential diagnosis constructed by a physician in a consult. Practicing medical specialists were integral in the development and validation of this model. Clinical ted to the development of a stable probabilistic framework utilizing symptom-disease associations to mathematically represent medical domain knowledge. The current iteration relies on static, manually curated values for calculating the degree of association. Shifting to real-world data-derived values represents the next step in model development. ©Shahrukh Chishti, Karan Raj Jaggi, Anuj Saini, Gaurav Agarwal, Ashish Ranjan. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 28.04.2020.BACKGROUND Wearable fitness trackers are devices that can record and enhance physical activity among users. Recently, photoplethysmography (PPG) devices that use optical heart rate sensors to detect heart rate in real time have become popular and help in monitoring and controlling exercise intensity. Although the benefits of using optical heart rate monitors have been highlighted through studies, the accuracy of the readouts these commercial devices generate has not been widely assessed for different age groups, especially for the East Asian population with Fitzpatrick skin type III or IV. OBJECTIVE This study aimed to examine the accuracy of 2 wearable fitness trackers with PPG to monitor heart rate in real time during moderate exercise in young and older adults. METHODS A total of 20 young adults and 20 older adults were recruited for this study. All participants were asked to undergo a series of sedentary and moderate physical activities using indoor aerobic exercise equipment. In this study, the Polar H7 6 (Young) and 0.73 (Senior). However, the results obtained using the Bland-Altman analysis indicated that both test optical devices underestimated the average heart rate. More importantly, the study documented some unexpected outlier readings reported by these devices when used on certain participants. CONCLUSIONS The study reveals that commonly used optical heart rate sensors, such as the ones used herein, generally produce accurate heart rate readings irrespective of the age of the user. However, users should avoid relying entirely on these readings to indicate exercise intensities, as these devices have a tendency to produce erroneous, extreme readings, which might misinterpret the real-time exercise intensity. Future studies should therefore emphasize the occurrence rate of such errors, as this will likely benefit the development of improved models of heart rate sensors. ©Hsueh-Wen Chow, Chao-Ching Yang. Originally published in JMIR mHealth and uHealth (http//mhealth.jmir.org), 28.04.2020.BACKGROUND Societies around the world are aging. Widespread aging creates problems for social services and health care practices. In this light, research on connected health (CH) is becoming essential. CH refers to a variety of technological measures that allow health care to be provided remotely with the aim of increasing efficiency, cost-effectiveness, and satisfaction on the part of health care recipients. CH is reshaping health care's direction to be more proactive, more preventive, and more precisely targeted and, thus, more effective. CH has been demonstrated to have great value in managing and preventing chronic diseases, which create huge burdens on health care and social services. In short, CH provides promising solutions to diseases and social challenges associated with aging populations. However, there are many barriers that need to be overcome before CH can be successfully and widely implemented. OBJECTIVE The research question of this study is as follows How can CH facilitate smart, remote, and targeted health care? The objective is to identify how health care can be managed in more comprehensive ways, such as by providing timely, flexible, accessible, and personalized services to preserve continuity and offer high-quality seamless health care.